





import glob
import os
import random
from string import ascii_lowercase, digits

import cva
_UpperCamelCase							=			""""""
_UpperCamelCase							=			""""""
_UpperCamelCase							=			""""""
_UpperCamelCase							=			1  # (0 is vertical, 1 is horizontal)







def    lowerCAmelCase__(						)			->     List[Any]:
  __snake_case     , __snake_case :					Union[str, Any]          =							get_dataset(lowercase ,  lowercase					)
  print("Processing..."					)
  __snake_case     , __snake_case     , __snake_case :					Dict          =							update_image_and_anno(lowercase ,  lowercase ,  lowercase					)

  for index, image in enumerate(lowercase					):
    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    __snake_case :					Dict          =							random_chars(32					)
    __snake_case :					List[str]          =							paths[index].split(os.sep					)[-1].rsplit("." ,  1					)[0]
    __snake_case :					Tuple          =							f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
    cva.imwrite(f"""/{file_root}.jpg""" ,  lowercase ,  [cva.IMWRITE_JPEG_QUALITY, 85]					)
    print(f"""Success {index+1}/{len(lowercase					)} with {file_name}"""					)
    __snake_case :					str          =							[]
    for anno in new_annos[index]:
      __snake_case :					List[str]          =							f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
      annos_list.append(lowercase					)
    with open(f"""/{file_root}.txt""" ,  "w"					) as outfile:
      outfile.write("\n".join(line for line in annos_list					)					)







def    lowerCAmelCase__(						lowercase							:     Optional[int] ,  lowercase							:     Optional[int]					)			->     Optional[int]:
  __snake_case :					Any          =							[]
  __snake_case :					Dict          =							[]
  for label_file in glob.glob(os.path.join(lowercase ,  "*.txt"					)					):
    __snake_case :					List[str]          =							label_file.split(os.sep					)[-1].rsplit("." ,  1					)[0]
    with open(lowercase					) as in_file:
      __snake_case :					Dict          =							in_file.readlines()
    __snake_case :					Tuple          =							os.path.join(lowercase ,  f"""{label_name}.jpg"""					)

    __snake_case :					List[Any]          =							[]
    for obj_list in obj_lists:
      __snake_case :					Any          =							obj_list.rstrip("\n"					).split(" "					)
      boxes.append(
          [
              int(obj[0]					),
              float(obj[1]					),
              float(obj[2]					),
              float(obj[3]					),
              float(obj[4]					),
          ]					)
    if not boxes:
      continue
    img_paths.append(lowercase					)
    labels.append(lowercase					)
  return img_paths, labels







def    lowerCAmelCase__(						lowercase							:     Union[str, Any] ,  lowercase							:     Optional[int] ,  lowercase							:     Tuple = 1					)			->     Any:
  __snake_case :					Tuple          =							[]
  __snake_case :					Union[str, Any]          =							[]
  __snake_case :					Optional[int]          =							[]
  for idx in range(len(lowercase					)					):
    __snake_case :					Optional[int]          =							[]
    __snake_case :					Union[str, Any]          =							img_list[idx]
    path_list.append(lowercase					)
    __snake_case :					int          =							anno_list[idx]
    __snake_case :					Union[str, Any]          =							cva.imread(lowercase					)
    if flip_type == 1:
      __snake_case :					List[Any]          =							cva.flip(lowercase ,  lowercase					)
      for bbox in img_annos:
        __snake_case :					Tuple          =							1 - bbox[1]
        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]					)
    elif flip_type == 0:
      __snake_case :					Any          =							cva.flip(lowercase ,  lowercase					)
      for bbox in img_annos:
        __snake_case :					Optional[Any]          =							1 - bbox[2]
        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]					)
    new_annos_lists.append(lowercase					)
    new_imgs_list.append(lowercase					)
  return new_imgs_list, new_annos_lists, path_list







def    lowerCAmelCase__(						lowercase							:     Any = 32					)			->     Union[str, Any]:
  assert number_char > 1, "The number of character should greater than 1"
  __snake_case :					List[str]          =							ascii_lowercase + digits
  return "".join(random.choice(lowercase					) for _ in range(lowercase					)					)


if __name__ == "__main__":
     main()
     print('''DONE ✅''')


"""simple docstring"""



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

SCREAMING_SNAKE_CASE__:Union[str, Any]									=			2






class  snake_case__      :


 def __init__( self    ,		*,  # begin keyword-only arguments
     lowerCamelCase="<s>"    ,		lowerCamelCase="<pad>"    ,		lowerCamelCase="</s>"    ,		lowerCamelCase="<unk>"    ,		lowerCamelCase=None    ,		):
        __a ,   __a ,   __a ,   __a       =    bos, unk, pad, eos
        __a       =    []
        __a       =    []
        __a       =    {}
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        if extra_special_symbols:
               for s in extra_special_symbols:
                      self.add_symbol(lowerCamelCase						)
        __a       =    len(self.symbols						)


 def __eq__( self    ,		lowerCamelCase						):
        return self.indices == other.indices


 def __getitem__( self    ,		lowerCamelCase						):
        if idx < len(self.symbols						):
               return self.symbols[idx]
        return self.unk_word


 def __len__( self						):
        return len(self.symbols						)


 def __contains__( self    ,		lowerCamelCase						):
        return sym in self.indices


 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase						):
        __a       =    cls()
        d.add_from_file(lowerCamelCase						)
        return d


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=1    ,		lowerCamelCase=False						):
        if word in self.indices and not overwrite:
               __a       =    self.indices[word]
               __a       =    self.count[idx] + n
               return idx
        else:
               __a       =    len(self.symbols						)
               __a       =    idx
               self.symbols.append(lowerCamelCase						)
               self.count.append(lowerCamelCase						)
               return idx


 def 						a__   ( self    ,		lowerCamelCase						):
        return 0






 def 						a__   ( self    ,		lowerCamelCase						):
        if isinstance(lowerCamelCase    ,		lowerCamelCase						):
               try:
                      with open(lowerCamelCase    ,		"r"    ,		encoding="utf-8"						) as fd:
                             self.add_from_file(lowerCamelCase						)
               except FileNotFoundError as fnfe:
                      raise fnfe
               except UnicodeError:
                      raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(lowerCamelCase						)						)
               return

        __a       =    f.readlines()
        __a       =    self._load_meta(lowerCamelCase						)

        for line in lines[indices_start_line:]:
               try:
                      __a ,   __a       =    line.rstrip().rsplit(" "    ,		1						)
                      if field == "#fairseq:overwrite":
                             __a       =    True
                             __a ,   __a       =    line.rsplit(" "    ,		1						)
                      else:
                             __a       =    False
                      __a       =    int(lowerCamelCase						)
                      __a       =    line
                      if word in self and not overwrite:
                             raise RuntimeError(
                                 "Duplicate word found when loading Dictionary: '{}'. "
                                 "Duplicate words can overwrite earlier ones by adding the "
                                 "#fairseq:overwrite flag at the end of the corresponding row "
                                 "in the dictionary file. If using the Camembert model, please "
                                 "download an updated copy of the model file.".format(lowerCamelCase						)						)
                      self.add_symbol(lowerCamelCase    ,		n=lowerCamelCase    ,		overwrite=lowerCamelCase						)
               except ValueError:
                      raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"						)



def 	_lowerCamelCase(	a				):
       # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
       # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
       __a       =    dict((re.sub(R"@@$"   ,    ""   ,    a				), v) if k.endswith("@@"				) else (re.sub(R"$"   ,    "</w>"   ,    a				), v) for k, v in d.items()				)
       __a       =    "<s> <pad> </s> <unk>".split()
       # restore the special tokens
       for k in keep_keys:
              del da[F"{k}</w>"]
              __a       =    d[k]  # restore
       return da



def 	_lowerCamelCase(	a   ,    a				):
       # prep
       if not os.path.exists(a				):
              raise ValueError(F"path {biogpt_checkpoint_path} does not exist!"				)
       os.makedirs(a   ,    exist_ok=a				)
       print(F"Writing results to {pytorch_dump_folder_path}"				)

       # handle various types of models

       __a       =    os.path.join(a   ,    "checkpoint.pt"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {checkpoint_file} does not exist!"				)
       __a       =    torch.load(a   ,    map_location="cpu"				)

       __a       =    chkpt["cfg"]["model"]

       # dicts
       __a       =    os.path.join(a   ,    "dict.txt"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {dict_file} does not exist!"				)
       __a       =    Dictionary.load(a				)
       __a       =    rewrite_dict_keys(src_dict.indices				)
       __a       =    len(a				)
       __a       =    os.path.join(a   ,    VOCAB_FILES_NAMES["vocab_file"]				)
       print(F"Generating {src_vocab_file} of {src_vocab_size} records"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # merges_file (bpecodes)
       __a       =    os.path.join(a   ,    "bpecodes"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {bpecodes_file} does not exist!"				)

       __a       =    os.path.join(a   ,    VOCAB_FILES_NAMES["merges_file"]				)
       shutil.copyfile(a   ,    a				)

       # model config
       __a       =    os.path.join(a   ,    "config.json"				)

       __a       =    {
           "activation_dropout": args["activation_dropout"],
           "architectures": ["BioGptForCausalLM"],
           "attention_probs_dropout_prob": args["attention_dropout"],
           "bos_token_id": 0,
           "eos_token_id": 2,
           "hidden_act": args["activation_fn"],
           "hidden_dropout_prob": args["dropout"],
           "hidden_size": args["decoder_embed_dim"],
           "initializer_range": 0.02,
           "intermediate_size": args["decoder_ffn_embed_dim"],
           "layer_norm_eps": 1E-1_2,
           "layerdrop": args["decoder_layerdrop"],
           "max_position_embeddings": args["max_target_positions"],
           "model_type": "biogpt",
           "num_attention_heads": args["decoder_attention_heads"],
           "num_hidden_layers": args["decoder_layers"],
           "pad_token_id": 1,
           "scale_embedding": not args["no_scale_embedding"],
           "tie_word_embeddings": args["share_decoder_input_output_embed"],
           "vocab_size": src_vocab_size,
       }

       # good hparam defaults to start with

       print(F"Generating {biogpt_model_config_file}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # tokenizer config
       __a       =    os.path.join(a   ,    a				)

       __a       =    {
           "bos_token": "<s>",
           "eos_token": "</s>",
           "model_max_length": 1_0_2_4,
           "pad_token": "<pad>",
           "special_tokens_map_file": None,
           "tokenizer_class": "BioGptTokenizer",
           "unk_token": "<unk>",
       }

       print(F"Generating {biogpt_tokenizer_config_file}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # model
       __a       =    chkpt["model"]

       # remove unneeded keys
       __a       =    [
           "decoder.version",
       ]
       for k in ignore_keys:
              model_state_dict.pop(a   ,    a				)

       __a       =    list(model_state_dict.keys()				)
       for layer_name in layer_names:
              if layer_name.endswith("output_projection.weight"				):
                     __a       =    model_state_dict.pop(a				)
              else:
                     __a       =    model_state_dict.pop(a				)

       __a       =    BioGptConfig.from_pretrained(a				)
       __a       =    BioGptForCausalLM(a				)

       # check that it loads ok
       model_new.load_state_dict(a				)

       # save
       __a       =    os.path.join(a   ,    a				)
       print(F"Generating {pytorch_weights_dump_path}"				)
       torch.save(a   ,    a				)

       print("Conversion is done!"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Any									=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--biogpt_checkpoint_path""",
       default=None,
       type=str,
       required=True,
       help=(
           """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
           """ bpecodes, etc."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   SCREAMING_SNAKE_CASE__:Any									=			parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
